`iL DdZddlZddlZddlZddlmZddlmZm Z ddl Z ddl m Z m Z ddl mZddlmZdd lmZdd lmZdd lmZmZmZdd lmZmZmZmZdd lmZddl m!Z!ej"e#Z$eedGddeZ%eedGddeZ&eedGddeZ'eedGddeZ(dZ)dZ*dLd!e j d"e+d#e,d$e j fd%Z-Gd&d'e j.Z/Gd(d)e j.Z0Gd*d+e j.Z1Gd,d-e j.Z2Gd.d/e j.Z3Gd0d1e j.Z4Gd2d3e j.Z5Gd4d5e j.Z6Gd6d7e j.Z7Gd8d9e j.Z8Gd:d;eZ9Gd<d=e j.Z:eGd>d?eZ;eGd@dAe;Z<edBGdCdDe;Z=edEGdFdGe;Z>edHGdIdJe;eZ?gdKZ@dS)Mz!PyTorch Swinv2 Transformer model.N) dataclass)OptionalUnion)Tensornn)ACT2FN)GradientCheckpointingLayer)BackboneOutput)PreTrainedModel) find_pruneable_heads_and_indicesmeshgridprune_linear_layer) ModelOutputauto_docstringlogging torch_int) BackboneMixin) Swinv2ConfigzP Swinv2 encoder's outputs, with potential hidden states and attentions. ) custom_introceZdZUdZdZeejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<dS)Swinv2EncoderOutputa reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nlast_hidden_state. hidden_states attentionsreshaped_hidden_states) __name__ __module__ __qualname____doc__rrtorch FloatTensor__annotations__rtuplerr~/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/swinv2/modeling_swinv2.pyrr*s6:x 12999=AM8E%"3S"89:AAA:>Ju0#567>>>FJHU5+